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arXiv 2019年

作者： Tang, Lvbang Wang, Hesheng Li, Peng Wang, Yong Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Beijing Institute of Control Engineering

In this paper, we propose a time-efficient approach to generate safe, smooth and dynamically feasible trajectories for quadrotors in the obstacle-cluttered environment. By using the uniform B-spline to represent trajectories, we transform the trajectory planning to a graph-search problem of B-spline control points in discretized space. Highly strict convex hull property of B-spline is derived to guarantee the dynamical feasibility of the entire trajectory. A novel non-uniform kinodynamic search strategy is adopted, and the step length is dynamically adjusted during the search process according to the Euclidean signed distance field (ESDF), making the trajectory achieve reasonable time-allocation and be away from obstacles. Nonstatic initial and goal states are allowed, therefore it can be used for online local replanning as well as global planning. Extensive simulation and hardware experiments show that our method achieves higher performance compared with the state-of-the-art method. Copyright © 2019, The Authors. All rights reserved.

关键词： Trajectories

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Tailoring memory performance via engineering conjugated bridges in benzo[c]thiadiazole based donor–acceptor small molecules

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ChemPhysMater 2025年

作者： Wang, Xianglin Lian, Hong Zhao, Liang Qin, Zhitao Yang, Yongge Xiao, Tianxiao Wang, Shuanglong Dong, Qingchen MOE Key Laboratory of Advanced Display and System Applications Shanghai University No.149 Yanchang Road Jingan District Shanghai 200072 China School of Mechanical & Electronic Engineering and Automation Shanghai University No.99 Shangda Road Baoshan District Shanghai 200444 China School of Microelectronics Shanghai University No. 235 Chengbei Road Jiading District Shanghai 201800 China MOE Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology No.79 Yingze West Street Taiyuan 030024 China Faculty of Informatics and Engineering The University of Electro-Communications Tokyo 182-8585 Japan Department of Electronic Engineering The Chinese University of Hong Kong New Territories Hong Kong 999077 China

Tuning the conjugated bridges between the electron-donor and electron-acceptor moieties plays a crucial role in enhancing the memristive properties of organic materials, yet it is rarely reported. Herein, we designed and synthesized four donor–acceptor (D-A) organic small molecules, namely 4,7-bis(4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)benzo[c][1,2,5]thiadiazole (DF-BT), 4,7-bis((4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)ethynyl)benzo[c][1,2,5]thiadiazole (DF-ynl-BT), 4,7-bis(5-(4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)thiophen-2-yl)benzo[c][1,2,5]thiadiazole (DF-Th-BT), and 4,7-bis((5-(4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)thiophen-2-yl)ethynyl)benzo[c][1,2,5]thiadiazole (DF-Th-ynl-BT), featuring unique conjugated bridges. These molecules were employed as active layers in resistive random-access memory (RRAM) devices to systematically investigate the influence of conjugation bridges on the electrical parameters. The results revealed that devices based on DF-BT, DF-ynl-BT, and DF-Th-BT exhibited write-once-read-many-times (WORM) characteristics, while the DF-Th-ynl-BT-based device demonstrated stable Flash-type switching behavior. Compared to DF-BT, memory devices utilizing DF-ynl-BT, DF-Th-BT, and DF-Th-ynl-BT, which incorporate additional conjugated bridges, exhibited nonvolatile memory properties with reduced threshold voltages, an improved ON/OFF current ratio, enhanced stability, and better uniformity. These findings demonstrated that tailoring the conjugated bridges in D-A molecules can effectively modulate resistive memory behavior and enhance device performance. Furthermore, the DF-Th-ynl-BT-based device was successfully integrated into logic gate circuits and display functions, highlighting its significant potential for applications in artificial intelligence (AI) neural networks. © 2025

关键词： Charge transfer Conjugated bridge Flash Memory device WORM

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State of charge estimation of parallel connected battery cells via descriptor system theory

arXiv

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arXiv 2020年

作者： Zhang, Dong Couto, Luis D. Benjamin, Sebastien Zeng, Wente Coutinho, Daniel F. Moura, Scott J. Department of Civil and Environmental Engineering University of California BerkeleyCA United States Department of Control Engineering and System Analysis Université Libre de Bruxelles BrusselsB-1050 Belgium Saft Batteries Total S.A. Total S.A. Department of Automation and Systems UFSC PO BOX 476 88040-900 Florianópolis SC Brazil

— This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation when multiple cells are connected in parallel, using only terminal voltage and total current measurements. For battery packs consisting of thousands of cells, it is desirable to estimate individual SOCs by only monitoring the total current in order to reduce sensing cost. Mathematically, series connected cells yield dynamics given by ordinary differential equations under classical full voltage sensing. In contrast, parallel connected cells are evidently more challenging because the dynamics are governed by a nonlinear descriptor system, including differential equations and algebraic equations arising from voltage and current balance across cells. An observer with linear output error injection is formulated, where the individual cell SOCs and local currents are locally observable from the total current and voltage measurements. The asymptotic convergence of differential and algebraic states is established by considering local Lipschitz continuity property of system nonlinearities. Simulation results on LiNiMnCoO2/Graphite (NMC) cells illustrate convergence for SOCs, local currents, and terminal voltage. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear equations

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Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network 23rd

Cerebrovascular Segmentation in MRA via Reverse Edge Attenti...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Hao Xia, Likun Song, Ran Yang, Jianlong Hao, Huaying Liu, Jiang Zhao, Yitian College of Information Engineering Capital Normal University Beijing China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China School of Control Science and Engineering Shandong University Jinan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical Interdisciplinary Capital Normal University Beijing China Laboratory of Neural Computing and Intelligent Perception Capital Normal University Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Beijing China

ISBN: (纸本)9783030597245

Automated extraction of cerebrovascular is of great importance in understanding the mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However, segmentation of cerebrovascular networks from magnetic resonance angiography (MRA) imagery continues to be challenging because of relatively poor contrast and inhomogeneous backgrounds, and the anatomical variations, complex geometry and topology of the networks themselves. In this paper, we present a novel cerebrovascular segmentation framework that consists of image enhancement and segmentation phases. We aim to remove redundant features, while retaining edge information in shallow features when combining these with deep features. We first employ a Retinex model, which is able to model noise explicitly to aid removal of imaging noise, as well as reducing redundancy within an image and emphasizing the vessel regions, thereby simplifying the subsequent segmentation problem. Subsequently, a reverse edge attention module is employed to discover edge information by paying particular attention to the regions that are not salient in high-level semantic features. The experimental results show that the proposed framework enables the reverse edge attention network to deliver a reliable cerebrovascular segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Image enhancement

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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DPNet: Dynamic Pooling Network for Tiny Object Detection

arXiv

引用

arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed downsampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware downsampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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A two-stage optimization model for floating offshore wind farm layout and control

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Renewable Energy 2025年 253卷

作者： Tao, Siyu Yang, Jisheng Zheng, Gang He, Ruiyang Feijóo-Lorenzo, Andrés E. College of Automation Engineering Nanjing University of Aeronautics and Astronautics No. 29 Jiangjun Road Nanjing211106 China Department of Building Environment and Energy Engineering Hong Kong Polytechnic University No. 102 Pokfulam Road Pok Fu Lam Hong Kong 999077 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China Monash Biomedical Imaging Monash University Clayton VIC3800 Australia School of Engineering Cardiff University CardiffCF243AA United Kingdom Department of Electrical Engineering University of Vigo Vigo36310 Spain

The effective planning of wind turbine (WT) positions in a wind farm (WF) plays a crucial role in capturing wind energy and increasing power generation. control optimization of WT attitudes can also help reduce wake effects and increase power generation, thereby maximizing the annual electricity production (AEP) of a WF. In this paper, a two-stage optimization model is proposed for planning the stationary layout of floating offshore wind farms (FOWFs), as well as the positions and attitudes of the floating offshore wind turbines (FOWTs) via optimal control. In the first stage, a competitive particle swarm optimizer (CPSO) is employed to generate multiple FOWF layouts. In the second stage, the genetic algorithm (GA) is utilized to optimize the yaw angles and axial induction factors of the FOWTs for operation control. Unlike the existing approaches that address WT layout design and operation control sequentially and separately, the proposed method integrates these aspects to simplify the FOWTs operation and to improve their performances. Additionally, taking the movable characteristics of the FOWTs into consideration, the proposed model incorporates the positional movement of the FOWTs under varying wind conditions, making the power calculations more realistic. Case studies demonstrate that compared with the traditional layout-only optimization, the proposed two-stage optimization model can help increase the AEP of the FOWFs by 4.72 %. © 2025 Elsevier Ltd

关键词： Genetic algorithms

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Corrigendum to "A comprehensive analysis of submarine groundwater discharge and nutrient fluxes in the Bohai Sea, China"

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Water research 2025年第PT B期268卷 122659页

作者： Xiaolang Zhang Hailong Li Xuejing Wang Xingxing Kuang Yan Zhang Kai Xiao Chao Xu Department of Geosciences Florida Atlantic University Boca Raton FL 33431 USA. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China. Electronic address: lihailong@***. Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin 300072 China. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China. State Key Laboratory of Biogeology and Environmental Geology and School of Water Resources and Environment China University of Geosciences-Beijing Beijing 100083 China. Department of Geosciences Texas Tech University Texas 79409 USA.

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Cluster-based Relevance Analysis Between Construction Mode and BCR for Optimizing the DAS

Cluster-based Relevance Analysis Between Construction Mode a...

引用

Data Driven control and Learning systems (DDCLS)

作者： Jiran Zhu Hua Leng Haiguo Tang Min Fan Yaling Liu Qing Yang College of Electronic and Information Engineering Tongji University Jiading Shanghai China State Grid Corporation of Hunan Electric Power corporation research institute Changsha Hunan China College of Electrical and Information Engineering Hunan University Changsha Hunan China School of Automation Chongqing University Chongqing Key Laboratory of Complex System Safety and Control (Ministry of Education) Chongqing University Chongqing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and operation of DAS, studying on the optimization of DAS has received much attention from both academia and industry. This study proposed cluster-based relevance analysis between construction mode and benefit-cost ratio (BCR) for optimizing the DAS. This method clusters the DASs of different cities, and analyzes the clustering results. Then, the method analyzes BCR of DAS in different categories and uses comparative analysis, grey relational analysis (GRA) and sensitivity analysis to analyze the relevance between the construction mode and BCR of typical city's DAS and determine the major factors affecting the BCR comprehensively. Finally, it integrates all analysis results to make optimization suggestions for DAS. The case study shows that the method is effective and feasible, which can provide direction and reference basis for the optimization of DAS in cities of different scale.

关键词： Urban areas Principal component analysis Investment Optimization Sensitivity analysis Power distribution automation

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Finite-size scaling of geometric renormalization flows in complex networks

arXiv

引用

arXiv 2020年

作者： Chen, Dan Su, Housheng Wang, Xiaofan Pan, Gui-Jun Chen, Guanrong School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China Department of Automation Shanghai University Shanghai200072 China Faculty of Physics and Electronic Science Hubei University Wuhan430062 China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong

Recently, the concept of geometric renormalization group provides a good approach for studying the structural symmetry and functional invariance of complex networks. Along this line, we systematically investigate the finite-size scaling of structural and dynamical observables in geometric renormalization flows of synthetic and real evolutionary networks. Our results show that these observables can be well characterized by a certain scaling function. Specifically, we show that the critical exponent implied by the scaling function is independent of these observables but only depends on the small-world properties of the network, namely, all networks located in the small-world phase have a uniform scaling exponent, while those located in the non-small-world phase and in their critical regions have another uniform scaling. More importantly, we perform extensive experiments on real evolutionary networks with small-world characteristics, and our results show that these observables also have uniform scaling in their geometric renormalization flows. Therefore, in a sense this exponent can be used as an effective measure for classifying universal small-world and non-small-world network models. © 2020, CC BY-SA.

关键词： Geometry

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